Overview

In this project, we will be looking at the player data provided by FIFA which contains information such as personal details, wages, physical attributes, technical skills, potential, and positional strengths. This is primarily data of FIFA 2018. Through this project, you will get a glimpse of insights behind the beautiful game and the kind of information and decisions a football manager goes through.

Objective

Preliminary Data Analysis. Explore the dataset and practice extracting basic observations about the data. The idea is for you to get comfortable working in Python. You are expected to do the following : Come up with the players' profile (characteristics of a player) of the different teams/countries. Generate a set of insights and recommendations that will help the coach to understand the competition.

Data Dictionary: Feature Explanation player index number

Name- name of a player Age - age of a player Nationality - player nationality

Overall - Overall Rating of the player Potential -Potential Rating of the player Club - The international club for which the player plays

Value - The market value of the player in the transfer market

Player Skills and other self-explanatory attributes

Special

Wage

Acceleration

Aggression

Agility

Balance

Ball control

Composure

Crossing

Curve

Dribbling

Finishing

Free kick accuracy

GK diving

GK handling

GK kicking

GK positioning

GK reflexes

Heading accuracy

Interceptions

Jumping

Long passing

Long shots

Marking

Penalties

Positioning

Reactions

Short passing

Shot power

Sliding tackle

Sprint speed

Stamina

Standing tackle

Strength

Vision

Volleys

CAM -Center Attacking Midfielder

CB - Center Back

CDM - Center Defensive Midfielder

CF - Center Forward

CM -Center Midfielder

ID - Player's ID in FIFA18

LAM - Left Attacking Midfielder

LB - Left Back

LCB - Left Center Back

LCM - Left Center Midfielder

LDM - Left Defensive Midfielder

LF -Left Forward

LM -Left Midfielder

LS -Left Striker

LW - Left-Wing

LWB - Left-Wing Back

Preferred Positions - Player's Preferred Position

RAM - Right Attacking Midfielder

RB - Right Back

RCB - Right Center Back

RCM - Right Center Midfielder

RDM - Right Defensive Midfielder

RF - Right Forward

RM - Right Midfielder

RS - Right Striker

RW - Right Wing

RWB - Right Wing Back

ST - Striker

Importing Packages and creating dataframe

In [1]:

check how many rows and columns are in the dataset

In [2]:
Out[2]:
(17981, 74)

Describe the dataset to get the stats of each column

In [3]:
Out[3]:
Unnamed: 0 Age Overall Potential Special CAM CB CDM CF CM LAM LB LCB LCM LDM LF LM LS LW LWB RAM RB RCB RCM RDM RF RM RS RW RWB ST
count 17981.00 17981.00 17981.00 17981.00 17981.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00 15952.00
mean 8990.00 25.14 66.25 71.19 1594.10 59.25 55.55 56.87 59.03 58.51 59.25 56.98 55.55 58.51 56.87 59.03 60.06 58.20 59.36 57.70 59.25 56.98 55.55 58.51 56.87 59.03 60.06 58.20 59.36 57.70 58.20
std 5190.81 4.61 6.99 6.10 272.15 9.88 12.19 10.31 9.93 8.89 9.88 9.79 12.19 8.89 10.31 9.93 9.35 9.18 9.98 9.14 9.88 9.79 12.19 8.89 10.31 9.93 9.35 9.18 9.98 9.14 9.18
min 0.00 16.00 46.00 46.00 728.00 27.00 25.00 26.00 27.00 30.00 27.00 30.00 25.00 30.00 26.00 27.00 28.00 31.00 26.00 31.00 27.00 30.00 25.00 30.00 26.00 27.00 28.00 31.00 26.00 31.00 31.00
25% 4495.00 21.00 62.00 67.00 1449.00 53.00 45.00 49.00 53.00 53.00 53.00 50.00 45.00 53.00 49.00 53.00 54.00 52.00 53.00 51.00 53.00 50.00 45.00 53.00 49.00 53.00 54.00 52.00 53.00 51.00 52.00
50% 8990.00 25.00 66.00 71.00 1633.00 60.00 57.00 58.00 60.00 59.00 60.00 58.00 57.00 59.00 58.00 60.00 61.00 59.00 60.00 58.00 60.00 58.00 57.00 59.00 58.00 60.00 61.00 59.00 60.00 58.00 59.00
75% 13485.00 28.00 71.00 75.00 1786.00 66.00 65.00 65.00 66.00 65.00 66.00 64.00 65.00 65.00 65.00 66.00 67.00 65.00 66.00 64.00 66.00 64.00 65.00 65.00 65.00 66.00 67.00 65.00 66.00 64.00 65.00
max 17980.00 47.00 94.00 94.00 2291.00 92.00 87.00 85.00 92.00 87.00 92.00 84.00 87.00 87.00 85.00 92.00 90.00 92.00 91.00 84.00 92.00 84.00 87.00 87.00 85.00 92.00 90.00 92.00 91.00 84.00 92.00

See what the data types are for each column

Create new Position column to hold other prefered positions if player has more than one, and show that the column was created.

In [4]:
Out[4]:
Unnamed: 0 Name Age Photo Nationality Flag Overall Potential Club Club Logo Value Wage Special Acceleration Aggression Agility Balance Ball control Composure Crossing Curve Dribbling Finishing Free kick accuracy GK diving GK handling GK kicking GK positioning GK reflexes Heading accuracy Interceptions Jumping Long passing Long shots Marking Penalties Positioning Reactions Short passing Shot power Sliding tackle Sprint speed Stamina Standing tackle Strength Vision Volleys CAM CB CDM CF CM LAM LB LCB LCM LDM LF LM LS LW LWB Preferred Positions RAM RB RCB RCM RDM RF RM RS RW RWB ST Position
0 0 Cristiano Ronaldo 32 https://cdn.sofifa.org/48/18/players/20801.png Portugal https://cdn.sofifa.org/flags/38.png 94 94 Real Madrid CF https://cdn.sofifa.org/24/18/teams/243.png 95.5M 565K 2228 89 63 89 63 93 95 85 81 91 94 76 7 11 15 14 11 88 29 95 77 92 22 85 95 96 83 94 23 91 92 31 80 85 88 89.00 53.00 62.00 91.00 82.00 89.00 61.00 53.00 82.00 62.00 91.00 89.00 92.00 91.00 66.00 ST LW 89.00 61.00 53.00 82.00 62.00 91.00 89.00 92.00 91.00 66.00 92.00 ST
1 1 L. Messi 30 https://cdn.sofifa.org/48/18/players/158023.png Argentina https://cdn.sofifa.org/flags/52.png 93 93 FC Barcelona https://cdn.sofifa.org/24/18/teams/241.png 105M 565K 2154 92 48 90 95 95 96 77 89 97 95 90 6 11 15 14 8 71 22 68 87 88 13 74 93 95 88 85 26 87 73 28 59 90 85 92.00 45.00 59.00 92.00 84.00 92.00 57.00 45.00 84.00 59.00 92.00 90.00 88.00 91.00 62.00 RW 92.00 57.00 45.00 84.00 59.00 92.00 90.00 88.00 91.00 62.00 88.00 RW
2 2 Neymar 25 https://cdn.sofifa.org/48/18/players/190871.png Brazil https://cdn.sofifa.org/flags/54.png 92 94 Paris Saint-Germain https://cdn.sofifa.org/24/18/teams/73.png 123M 280K 2100 94 56 96 82 95 92 75 81 96 89 84 9 9 15 15 11 62 36 61 75 77 21 81 90 88 81 80 33 90 78 24 53 80 83 88.00 46.00 59.00 88.00 79.00 88.00 59.00 46.00 79.00 59.00 88.00 87.00 84.00 89.00 64.00 LW 88.00 59.00 46.00 79.00 59.00 88.00 87.00 84.00 89.00 64.00 84.00 LW
3 3 L. Suárez 30 https://cdn.sofifa.org/48/18/players/176580.png Uruguay https://cdn.sofifa.org/flags/60.png 92 92 FC Barcelona https://cdn.sofifa.org/24/18/teams/241.png 97M 510K 2291 88 78 86 60 91 83 77 86 86 94 84 27 25 31 33 37 77 41 69 64 86 30 85 92 93 83 87 38 77 89 45 80 84 88 87.00 58.00 65.00 88.00 80.00 87.00 64.00 58.00 80.00 65.00 88.00 85.00 88.00 87.00 68.00 ST 87.00 64.00 58.00 80.00 65.00 88.00 85.00 88.00 87.00 68.00 88.00 ST
4 4 M. Neuer 31 https://cdn.sofifa.org/48/18/players/167495.png Germany https://cdn.sofifa.org/flags/21.png 92 92 FC Bayern Munich https://cdn.sofifa.org/24/18/teams/21.png 61M 230K 1493 58 29 52 35 48 70 15 14 30 13 11 91 90 95 91 89 25 30 78 59 16 10 47 12 85 55 25 11 61 44 10 83 70 11 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan GK nan nan nan nan nan nan nan nan nan nan nan GK

Convert columns to numerical types

In [5]:
In [6]:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 17981 entries, 0 to 17980
Data columns (total 75 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Unnamed: 0           17981 non-null  int64  
 1   Name                 17981 non-null  object 
 2   Age                  17981 non-null  int64  
 3   Photo                17981 non-null  object 
 4   Nationality          17981 non-null  object 
 5   Flag                 17981 non-null  object 
 6   Overall              17981 non-null  int64  
 7   Potential            17981 non-null  int64  
 8   Club                 17733 non-null  object 
 9   Club Logo            17981 non-null  object 
 10  Value                17981 non-null  int32  
 11  Wage                 17981 non-null  int32  
 12  Special              17981 non-null  int64  
 13  Acceleration         17897 non-null  float64
 14  Aggression           17913 non-null  float64
 15  Agility              17910 non-null  float64
 16  Balance              17924 non-null  float64
 17  Ball control         17840 non-null  float64
 18  Composure            17887 non-null  float64
 19  Crossing             17885 non-null  float64
 20  Curve                17908 non-null  float64
 21  Dribbling            17850 non-null  float64
 22  Finishing            17867 non-null  float64
 23  Free kick accuracy   17932 non-null  float64
 24  GK diving            17955 non-null  float64
 25  GK handling          17954 non-null  float64
 26  GK kicking           17962 non-null  float64
 27  GK positioning       17955 non-null  float64
 28  GK reflexes          17952 non-null  float64
 29  Heading accuracy     17906 non-null  float64
 30  Interceptions        17881 non-null  float64
 31  Jumping              17911 non-null  float64
 32  Long passing         17860 non-null  float64
 33  Long shots           17898 non-null  float64
 34  Marking              17869 non-null  float64
 35  Penalties            17951 non-null  float64
 36  Positioning          17886 non-null  float64
 37  Reactions            17866 non-null  float64
 38  Short passing        17832 non-null  float64
 39  Shot power           17908 non-null  float64
 40  Sliding tackle       17886 non-null  float64
 41  Sprint speed         17867 non-null  float64
 42  Stamina              17873 non-null  float64
 43  Standing tackle      17857 non-null  float64
 44  Strength             17877 non-null  float64
 45  Vision               17874 non-null  float64
 46  Volleys              17940 non-null  float64
 47  CAM                  15952 non-null  float64
 48  CB                   15952 non-null  float64
 49  CDM                  15952 non-null  float64
 50  CF                   15952 non-null  float64
 51  CM                   15952 non-null  float64
 52  LAM                  15952 non-null  float64
 53  LB                   15952 non-null  float64
 54  LCB                  15952 non-null  float64
 55  LCM                  15952 non-null  float64
 56  LDM                  15952 non-null  float64
 57  LF                   15952 non-null  float64
 58  LM                   15952 non-null  float64
 59  LS                   15952 non-null  float64
 60  LW                   15952 non-null  float64
 61  LWB                  15952 non-null  float64
 62  Preferred Positions  17981 non-null  object 
 63  RAM                  15952 non-null  float64
 64  RB                   15952 non-null  float64
 65  RCB                  15952 non-null  float64
 66  RCM                  15952 non-null  float64
 67  RDM                  15952 non-null  float64
 68  RF                   15952 non-null  float64
 69  RM                   15952 non-null  float64
 70  RS                   15952 non-null  float64
 71  RW                   15952 non-null  float64
 72  RWB                  15952 non-null  float64
 73  ST                   15952 non-null  float64
 74  Position             17981 non-null  object 
dtypes: float64(60), int32(2), int64(5), object(8)
memory usage: 10.2+ MB

Reduce dimension by combining position types and stats

In [7]:
In [8]:
In [9]:
Out[9]:
(17981, 22)
In [10]:
Out[10]:
Unnamed: 0 Age Value Wage Special Attack_rate Midfield_rate Defense_rate Passing Shooting Defending Speed Control GoalKeeping Mental Power Avg_rating
count 17981.00 17981.00 17981.00 17981.00 17981.00 15952.00 15952.00 15952.00 17954.00 17977.00 17955.00 17974.00 17960.00 17978.00 17975.00 17970.00 17981.00
mean 8990.00 25.14 2385389.58 11546.97 1594.10 58.62 58.67 56.57 53.47 47.96 45.89 63.59 53.46 16.59 54.07 64.65 68.72
std 5190.81 4.61 5353969.97 23080.00 272.15 9.48 8.28 10.33 15.31 15.58 20.97 11.33 17.29 17.09 13.00 7.47 6.02
min 0.00 16.00 0.00 0.00 728.00 30.00 31.00 28.00 10.00 7.00 6.00 23.00 6.00 1.00 11.00 25.00 46.00
25% 4495.00 21.00 300000.00 2000.00 1449.00 52.00 53.00 49.00 46.00 39.00 25.00 58.00 46.00 10.00 48.00 60.00 65.00
50% 8990.00 25.00 675000.00 4000.00 1633.00 60.00 59.00 58.00 57.00 51.00 51.00 66.00 57.00 11.00 56.00 65.00 68.00
75% 13485.00 28.00 2100000.00 12000.00 1786.00 66.00 64.00 65.00 64.00 59.00 64.00 72.00 66.00 12.00 63.00 70.00 72.00
max 17980.00 47.00 123000000.00 565000.00 2291.00 92.00 84.00 84.00 89.00 88.00 90.00 91.00 94.00 91.00 86.00 90.00 94.00

In the dataframe there are 2029 rows where there is no attack defense or midfield rate.

In [11]:
Out[11]:
Attack_rate            2029
Defense_rate           2029
Midfield_rate          2029
Club                    248
Passing                  27
Defending                26
Control                  21
Power                    11
Speed                     7
Mental                    6
Shooting                  4
GoalKeeping               3
Value                     0
Name                      0
Age                       0
Nationality               0
Avg_rating                0
Wage                      0
Special                   0
Preferred Positions       0
Position                  0
Unnamed: 0                0
dtype: int64
In [12]:
Out[12]:
Unnamed: 0 Name Age Nationality Club Value Wage Special Preferred Positions Position Attack_rate Midfield_rate Defense_rate Passing Shooting Defending Speed Control GoalKeeping Mental Power Avg_rating
1784 1784 A. Blake 26 Jamaica Philadelphia Union 6500000 7000 1146 GK GK nan nan nan 26.00 15.00 14.00 54.00 21.00 nan 23.00 56.00 78.00
11784 11784 Kang Hyeon Mu 22 Korea Republic Pohang Steelers 550000 2000 1097 GK GK nan nan nan 22.00 18.00 19.00 40.00 19.00 nan 35.00 56.00 69.00
17007 17007 A. Wichne 20 Norway Viking FK 110000 1000 1032 GK GK nan nan nan 25.00 18.00 16.00 43.00 14.00 nan 27.00 57.00 60.00
In [13]:
Out[13]:
Attack_rate            2029
Defense_rate           2029
Midfield_rate          2029
Club                     42
GoalKeeping               3
Mental                    1
Speed                     1
Power                     1
Special                   0
Name                      0
Age                       0
Nationality               0
Value                     0
Wage                      0
Avg_rating                0
Preferred Positions       0
Position                  0
Passing                   0
Shooting                  0
Defending                 0
Control                   0
Unnamed: 0                0
dtype: int64

There are also 2029 Goalkeepers in the data set, and they represent 100% of those 3 null values

Treat missing values

In [14]:
Out[14]:
Unnamed: 0 Name Age Nationality Club Value Wage Special Preferred Positions Position Attack_rate Midfield_rate Defense_rate Passing Shooting Defending Speed Control GoalKeeping Mental Power Avg_rating
1784 1784 A. Blake 26 Jamaica Philadelphia Union 6500000 7000 1146 GK GK nan nan nan 26.00 15.00 14.00 54.00 21.00 nan 23.00 56.00 78.00
11784 11784 Kang Hyeon Mu 22 Korea Republic Pohang Steelers 550000 2000 1097 GK GK nan nan nan 22.00 18.00 19.00 40.00 19.00 nan 35.00 56.00 69.00
17007 17007 A. Wichne 20 Norway Viking FK 110000 1000 1032 GK GK nan nan nan 25.00 18.00 16.00 43.00 14.00 nan 27.00 57.00 60.00
In [15]:

There are 3 rows missing this stat, and they are goal keepers, so remove these rows.

In [16]:

There are a few rows that have missing values, and since there are a lot less than the total rows i set them to the mean.

In [17]:
Out[17]:
Attack_rate            2026
Defense_rate           2026
Midfield_rate          2026
Club                    248
Position                  0
Name                      0
Age                       0
Nationality               0
Value                     0
Wage                      0
Special                   0
Preferred Positions       0
Avg_rating                0
Power                     0
Passing                   0
Shooting                  0
Defending                 0
Speed                     0
Control                   0
GoalKeeping               0
Mental                    0
Unnamed: 0                0
dtype: int64
In [18]:
In [19]:
Out[19]:
Unnamed: 0 Name Age Nationality Club Value Wage Special Preferred Positions Position Attack_rate Midfield_rate Defense_rate Passing Shooting Defending Speed Control GoalKeeping Mental Power Avg_rating
count 2026.00 2026 2026.00 2026 1984 2026.00 2026.00 2026.00 2026 2026 0.00 0.00 0.00 2026.00 2026.00 2026.00 2026.00 2026.00 2026.00 2026.00 2026.00 2026.00
unique nan 2004 nan 95 647 nan nan nan 1 1 nan nan nan nan nan nan nan nan nan nan nan nan
top nan K. Müller nan England Bournemouth nan nan nan GK GK nan nan nan nan nan nan nan nan nan nan nan nan
freq nan 2 nan 175 6 nan nan nan 2026 2026 nan nan nan nan nan nan nan nan nan nan nan nan
mean 10108.90 NaN 26.08 NaN NaN 1571771.96 7861.80 1050.36 NaN NaN nan nan nan 22.28 15.82 14.67 41.76 16.30 63.83 27.77 54.19 67.21
std 5471.43 NaN 5.40 NaN NaN 4296770.16 16392.97 126.67 NaN NaN nan nan nan 5.10 3.26 3.12 7.92 3.54 7.39 6.60 7.65 6.57
min 4.00 NaN 16.00 NaN NaN 0.00 0.00 728.00 NaN NaN nan nan nan 10.00 7.00 6.00 23.00 6.00 43.00 11.00 25.00 46.00
25% 5421.50 NaN 22.00 NaN NaN 140000.00 1000.00 970.25 NaN NaN nan nan nan 19.00 14.00 12.00 37.00 14.00 59.00 23.00 50.00 63.00
50% 10820.00 NaN 26.00 NaN NaN 375000.00 2000.00 1063.00 NaN NaN nan nan nan 21.00 16.00 15.00 42.00 16.00 64.00 26.00 55.00 67.00
75% 15160.00 NaN 30.00 NaN NaN 975000.00 7000.00 1134.75 NaN NaN nan nan nan 25.00 18.00 17.00 47.00 18.00 69.00 32.00 60.00 72.00
max 17977.00 NaN 47.00 NaN NaN 64500000.00 230000.00 1493.00 NaN NaN nan nan nan 49.00 35.00 29.00 63.59 35.00 91.00 54.07 74.00 92.00

Univariate Analysis

In [20]:
Out[20]:
count mean std min 25% 50% 75% max
Unnamed: 0 17978.00 8989.80 5190.58 0.00 4495.25 8989.50 13484.75 17980.00
Age 17978.00 25.14 4.61 16.00 21.00 25.00 28.00 47.00
Value 17978.00 2385389.36 5354284.35 0.00 300000.00 675000.00 2100000.00 123000000.00
Wage 17978.00 11548.34 23081.66 0.00 2000.00 4000.00 12000.00 565000.00
Special 17978.00 1594.18 272.10 728.00 1449.00 1633.00 1786.00 2291.00
Attack_rate 15952.00 58.62 9.48 30.00 52.00 60.00 66.00 92.00
Midfield_rate 15952.00 58.67 8.28 31.00 53.00 59.00 64.00 84.00
Defense_rate 15952.00 56.57 10.33 28.00 49.00 58.00 65.00 84.00
Passing 17978.00 53.48 15.29 10.00 46.00 57.00 64.00 89.00
Shooting 17978.00 47.97 15.57 7.00 39.00 51.00 59.00 88.00
Defending 17978.00 45.90 20.95 6.00 25.00 51.00 64.00 90.00
Speed 17978.00 63.59 11.33 23.00 58.00 66.00 72.00 91.00
Control 17978.00 53.47 17.28 6.00 46.00 57.00 66.00 94.00
GoalKeeping 17978.00 16.59 17.09 1.00 10.00 11.00 12.00 91.00
Mental 17978.00 54.07 13.00 11.00 48.00 56.00 63.00 86.00
Power 17978.00 64.66 7.47 25.00 60.00 65.00 70.00 90.00
Avg_rating 17978.00 68.72 6.02 46.00 65.00 68.00 72.00 94.00
In [21]:

Value

In [22]:
In [23]:
In [24]:
Out[24]:
Unnamed: 0             9253
Name                   9253
Age                    9253
Nationality            9253
Club                   9253
Value                  9253
Wage                   9253
Special                9253
Preferred Positions    9253
Position               9253
Attack_rate            8574
Midfield_rate          8574
Defense_rate           8574
Passing                9253
Shooting               9253
Defending              9253
Speed                  9253
Control                9253
GoalKeeping            9253
Mental                 9253
Power                  9253
Avg_rating             9253
dtype: int64

Value is right skewed with some players making drastically more than the average.

Wage

In [25]:
In [26]:

Wage is similar to Value in taht it is right skewed.

Special

In [27]:
In [28]:

Special is slightly left skewed

Attack Rate

In [29]:
In [30]:

Attack Rate is slightly left skewed, with a few minorm outliers on either side.

Defense Rate

In [31]:
In [32]:

Defense Rate is close to symetrical, with no outliers.

Midfield Rate

In [33]:
In [34]:
In [35]:
Out[35]:
count   15952.00
mean       58.67
std         8.28
min        31.00
25%        53.00
50%        59.00
75%        64.00
max        84.00
Name: Midfield_rate, dtype: float64

Midfield Rate is symetrical with some outliers on each side of the IQR.

Passing

In [36]:
In [37]:
In [38]:
Out[38]:
count   17978.00
mean       53.48
std        15.29
min        10.00
25%        46.00
50%        57.00
75%        64.00
max        89.00
Name: Passing, dtype: float64

Passing is left skewed with outliers on both sides

Shooting

In [39]:
In [40]:

Shooting is left swkewed with some outliers on the left, probably from people who play positions that dont shoot often

Defending

In [41]:
In [42]:

Defending is left skewed with no outliers.

Speed

In [43]:
In [44]:

Speed is slightly left skewed with outliers on the left

Control

In [45]:
In [46]:

Control is left skewed with outliers on the left

Goalkeeping

In [47]:

I use a seperate dataframe 'dfGK' below which only has players who ahev the Position 'GK', since goalkeeping stat is relevant only to them.

In [48]:
In [49]:

Goalkeeping is symettrical with a few outliers on each side

Mental

In [50]:
In [51]:

Mental is left skewed with outliers on the left

Power

In [52]:
In [53]:

Power is symetrical with outliers on each side

Average Rating

In [54]:
In [55]:

Average rating is roght skewed with outliers on each side

Bivariate Analysis

In [56]:

Average Rating is highly correlated with Midfield rate, attack rate, and value, showing that players with high skill in attacking or midfield positions are typicaly rated higher. Attack rate is highly correlated to control and shooting ability, as is midfield.

Plottting a few teams against each other since i cant do it for every team

In [57]:
In [58]:
In [59]:
In [60]:

Club vs Attack Rate

In [61]:

Barcelona has the highest average attack rate among the 4. The others will want to be mindful when they face them and concentrate on their defense.

Club vs Midfield Rate

In [62]:

Club vs Defense Rate

In [63]:

Club vs Value

In [64]:
In [65]:
Out[65]:
Value Wage
Club
Chelsea 673085000 3537000
FC Barcelona 744500000 4792000
Juventus 617099999 3165000
Manchester City 578215000 3152000

Barcelona spends significantly more money on its players wages than other teams and has the greatest player value. However the other teams have a better ratio of Value/Wage, showing that the extra bit of performance that Barcelonas team members have, comes with a higher cost.

In [66]:
Out[66]:
Unnamed: 0 Age Value Wage Special Attack_rate Midfield_rate Defense_rate Passing Shooting Defending Speed Control GoalKeeping Mental Power Avg_rating
count 25.00 25.00 25.00 25.00 25.00 22.00 22.00 22.00 25.00 25.00 25.00 25.00 25.00 25.00 25.00 25.00 25.00
mean 503.80 26.52 29780000.00 191680.00 1961.16 75.18 75.77 69.68 69.96 61.32 57.24 74.12 71.08 19.48 69.20 69.96 84.20
std 1082.26 3.65 24274077.39 116217.01 286.40 6.73 4.48 10.32 17.22 18.83 25.78 11.33 21.31 22.44 14.31 5.43 4.26
min 1.00 20.00 2000000.00 47000.00 1173.00 62.00 65.00 49.00 21.00 15.00 14.00 48.00 18.00 7.00 25.00 58.00 74.00
25% 78.00 23.00 17500000.00 135000.00 1969.00 70.25 72.50 64.00 72.00 59.00 32.00 72.00 72.00 10.00 69.00 66.00 82.00
50% 231.00 27.00 23500000.00 150000.00 2037.00 76.50 77.50 73.00 74.00 67.00 64.00 75.00 75.00 11.00 72.00 70.00 84.00
75% 358.00 30.00 35500000.00 215000.00 2109.00 78.00 78.00 78.00 78.00 71.00 78.00 80.00 84.00 13.00 79.00 75.00 87.00
max 5444.00 33.00 105000000.00 565000.00 2291.00 90.00 82.00 81.00 87.00 86.00 87.00 90.00 94.00 84.00 84.00 77.00 93.00
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Unnamed: 0 Age Value Wage Special Attack_rate Midfield_rate Defense_rate Passing Shooting Defending Speed Control GoalKeeping Mental Power Avg_rating
count 33.00 33.00 33.00 33.00 33.00 29.00 29.00 29.00 33.00 33.00 33.00 33.00 33.00 33.00 33.00 33.00 33.00
mean 3737.67 24.36 20396515.15 107181.82 1779.91 67.21 67.79 64.34 60.83 53.58 52.63 71.00 61.80 17.85 60.67 65.85 79.55
std 5665.42 4.78 21198294.50 78935.28 317.11 11.08 9.41 13.15 18.18 18.33 25.77 12.35 20.57 22.19 15.94 9.98 7.14
min 7.00 16.00 260000.00 4000.00 1008.00 44.00 48.00 39.00 15.00 15.00 12.00 39.00 15.00 4.00 29.00 34.00 66.00
25% 109.00 21.00 1400000.00 40000.00 1549.00 58.00 64.00 55.00 51.00 45.00 29.00 65.00 53.47 9.00 54.00 62.00 74.00
50% 529.00 24.00 14000000.00 105000.00 1858.00 69.00 70.00 64.00 65.00 57.00 52.00 75.00 68.00 10.00 65.00 68.00 81.00
75% 5876.00 27.00 31500000.00 170000.00 2069.00 76.00 75.00 76.00 73.00 68.00 76.00 80.00 76.00 12.00 72.00 72.00 85.00
max 15718.00 35.00 90500000.00 295000.00 2154.00 84.00 80.00 83.00 89.00 78.00 87.00 87.00 89.00 84.00 85.00 82.00 90.00
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Unnamed: 0 Age Value Wage Special Attack_rate Midfield_rate Defense_rate Passing Shooting Defending Speed Control GoalKeeping Mental Power Avg_rating
count 26.00 26.00 26.00 26.00 26.00 23.00 23.00 23.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00
mean 937.04 27.77 23734615.35 121730.77 1898.96 71.22 72.00 69.39 66.19 59.15 57.42 73.50 67.08 15.62 67.27 72.23 82.85
std 1799.10 4.48 20236065.71 58305.79 299.85 9.30 6.68 12.13 16.98 19.47 28.92 10.79 20.65 23.07 13.02 5.90 4.92
min 9.00 20.00 700000.00 34000.00 1062.00 54.00 61.00 46.00 22.00 14.00 13.00 43.00 15.00 3.00 32.00 57.00 69.00
25% 85.50 24.00 8500000.00 88250.00 1761.00 63.00 66.00 58.00 60.25 49.25 25.50 68.50 58.50 5.25 61.25 70.25 81.25
50% 189.50 27.50 20750000.00 112500.00 1978.00 74.00 74.00 74.00 72.50 66.00 71.50 77.00 72.50 8.50 71.50 72.00 83.50
75% 762.50 30.00 30125000.00 156250.00 2110.75 78.00 77.00 77.50 77.75 73.50 81.75 80.50 81.75 11.00 76.00 75.00 85.00
max 6486.00 39.00 79000000.00 275000.00 2210.00 86.00 82.00 83.00 85.00 83.00 90.00 86.00 91.00 85.00 83.00 81.00 90.00
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Unnamed: 0 Age Value Wage Special Attack_rate Midfield_rate Defense_rate Passing Shooting Defending Speed Control GoalKeeping Mental Power Avg_rating
count 33.00 33.00 33.00 33.00 33.00 30.00 30.00 30.00 33.00 33.00 33.00 33.00 33.00 33.00 33.00 33.00 33.00
mean 5199.27 23.45 17521666.67 95515.15 1785.27 65.90 66.43 61.93 62.36 54.48 51.00 71.03 63.82 16.15 62.24 68.09 78.24
std 6567.62 5.15 20613445.43 83302.96 321.52 13.31 11.94 12.48 16.91 18.49 23.06 10.80 19.99 19.10 14.72 6.90 8.45
min 11.00 17.00 60000.00 5000.00 1066.00 32.00 35.00 38.00 22.00 15.00 13.00 43.00 19.00 1.00 27.00 54.00 60.00
25% 161.00 19.00 625000.00 15000.00 1515.00 60.00 57.25 51.75 51.00 40.00 35.00 63.00 52.00 10.00 49.00 64.00 72.00
50% 756.00 22.00 10500000.00 95000.00 1913.00 66.50 71.00 60.00 69.00 59.00 49.00 71.00 69.00 11.00 66.00 70.00 82.00
75% 11445.00 27.00 26000000.00 140000.00 2073.00 77.75 75.75 75.25 75.00 71.00 70.00 78.00 78.00 12.00 74.00 73.00 85.00
max 17961.00 34.00 83000000.00 325000.00 2164.00 86.00 82.00 80.00 88.00 81.00 83.00 87.00 87.00 81.00 82.00 82.00 90.00

Conclusion

Insights and recommendations:

FC barcelona out performs most of the other teams in most metrics, having the highest averaage Attack, Defense, and Midfield rates, as well as Passing, Shooting, and Average Player Rating. However their Goalkeeping rating is less than or equal than all but Manchester City's.

Chelsea and Manchester having slightly weaker attack scores, so they could benefit from training or rectuiting more in that area. Looking at the team vs value graph, Barcelona has the greatest value, which reflects in its higher performance ratings. In order to compete against them other teams will either have to improve the team members it already has, or try and recruit more skilled players, which could cost more money.

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